arXiv subjects

Xin Jin

Publications and source records attributed to Xin Jin.

At least 325 records · Page 18Linked to original sources

arXiv2022

Strong and tunable magnetoelectric coupling in 2D trilayer heterostructures

The quest for electric-field control of nanoscale magnetic states such as skyrmions, which would impact the field of spintronics, has led to a challenging search for multiferroic materials or structures with strong magnetoelectric coupling and efficient electric-field control. Here we report a theoretical prediction that such phenomena can be realized in two-dimensional (2D) bilayer FE/PMM and trilayer FE/PMM/FE heterostructures (two-terminal and three-terminal devices), where FE is a 2D ferroelectric and PMM is a polar magnetic metal with strong spin-orbit coupling. Such a PMM has strong Dzyaloshinskii-Moriya interactions (DMI) that can generate skyrmions, while the FE can generate strong magnetoelectric coupling through polarization-polarization interactions. In trilayer heterostructures, contact to the metallic PMM layer enables multiple polarization configurations for electric-field control of skyrmions. We report density-functional-theory calculations for particular material choices that demonstrate the effectiveness of these arrangements, with the key driver being the polarization-polarization interactions between the PMM and FE layers. The present findings provide a method to achieve strong magnetoelectric coupling in the 2D limit and a new perspective for the design of related spintronics.

cond-mat.mtrl-sci↗

arXiv2022

Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation

In unsupervised domain adaptation (UDA), directly adapting from the source to the target domain usually suffers significant discrepancies and leads to insufficient alignment. Thus, many UDA works attempt to vanish the domain gap gradually and softly via various intermediate spaces, dubbed domain bridging (DB). However, for dense prediction tasks such as domain adaptive semantic segmentation (DASS), existing solutions have mostly relied on rough style transfer and how to elegantly bridge domains is still under-explored. In this work, we resort to data mixing to establish a deliberated domain bridging (DDB) for DASS, through which the joint distributions of source and target domains are aligned and interacted with each in the intermediate space. At the heart of DDB lies a dual-path domain bridging step for generating two intermediate domains using the coarse-wise and the fine-wise data mixing techniques, alongside a cross-path knowledge distillation step for taking two complementary models trained on generated intermediate samples as 'teachers' to develop a superior 'student' in a multi-teacher distillation manner. These two optimization steps work in an alternating way and reinforce each other to give rise to DDB with strong adaptation power. Extensive experiments on adaptive segmentation tasks with different settings demonstrate that our DDB significantly outperforms state-of-the-art methods. Code is available at https://github.com/xiaoachen98/DDB.git.

cs.CV↗

arXiv2022

Confounder Identification-free Causal Visual Feature Learning

Confounders in deep learning are in general detrimental to model's generalization where they infiltrate feature representations. Therefore, learning causal features that are free of interference from confounders is important. Most previous causal learning based approaches employ back-door criterion to mitigate the adverse effect of certain specific confounder, which require the explicit identification of confounder. However, in real scenarios, confounders are typically diverse and difficult to be identified. In this paper, we propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders. CICF models the interventions among different samples based on front-door criterion, and then approximates the global-scope intervening effect upon the instance-level interventions from the perspective of optimization. In this way, we aim to find a reliable optimization direction, which avoids the intervening effects of confounders, to learn causal features. Furthermore, we uncover the relation between CICF and the popular meta-learning strategy MAML, and provide an interpretation of why MAML works from the theoretical perspective of causal learning for the first time. Thanks to the effective learning of causal features, our CICF enables models to have superior generalization capability. Extensive experiments on domain generalization benchmark datasets demonstrate the effectiveness of our CICF, which achieves the state-of-the-art performance.

cs.LG↗

arXiv2022

Floquet Engineering of Nonequilibrium Valley-Polarized Quantum Anomalous Hall Effect with Tunable Chern Number

Numerous attempts have been made so far to explore the quantum anomalous Hall effect (QAHE), but the ultralow observed temperature strongly hinders its practical applications. Hence, it is of great interest to go beyond the existing paradigm of QAHE. Here, we propose that Floquet engineering offers a strategy to realize the QAHE via hybridization of Floquet-Bloch bands. Based on first-principles calculations and Floquet theorem, we unveil that nonequilibrium valley-polarized QAHE (VP-QAHE), independent of magnetic orders, is widely present in ferromagnetic and nonmagnetic members of two-dimensional family materials $M$Si$_2$$Z_4$ ($M$ = Mo, W, V; $Z$ = N, P, As) by irradiating circularly polarized light (CPL). Remarkably, by tuning the frequency, intensity, and handedness of incident CPL, the Chern number of VP-QAHE is highly tunable and up to $\mathcal{C}=\pm 4$. We reveal that such Chern number tunable VP-QAHE attributes to light-induced trigonal warping and multiple band inversion at different valleys. The valley-resolved chiral edge states and quantized plateau of Hall conductance, which facilitates the experimental measurement, are visible inside the global band gap. Our work not only establishes Floquet Engineering of nonequilibrium VP-QAHE with tunable Chern number in realistic materials, but also provides a promising avenue to explore emergent topological phases under light irradiation.

cond-mat.mes-hall↗

arXiv2022

Orloj: Predictably Serving Unpredictable DNNs

Existing DNN serving solutions can provide tight latency SLOs while maintaining high throughput via careful scheduling of incoming requests, whose execution times are assumed to be highly predictable and data-independent. However, inference requests to emerging dynamic DNNs -- e.g., popular natural language processing (NLP) models and computer vision (CV) models that skip layers -- are data-dependent. They exhibit poor performance when served using existing solutions because they experience large variance in request execution times depending on the input -- the longest request in a batch inflates the execution times of the smaller ones, causing SLO misses in the absence of careful batching. In this paper, we present Orloj, a dynamic DNN serving system, that captures this variance in dynamic DNNs using empirical distributions of expected request execution times, and then efficiently batches and schedules them without knowing a request's precise execution time. Orloj significantly outperforms state-of-the-art serving solutions for high variance dynamic DNN workloads by 51--80% in finish rate under tight SLO constraints, and over 100% under more relaxed SLO settings. For well-studied static DNN workloads, Orloj keeps comparable performance with the state-of-the-art.

cs.DC↗

arXiv2022

Aesthetics Driven Autonomous Time-Lapse Photography Generation by Virtual and Real Robots

Time-lapse photography is employed in movies and promotional films because it can reflect the passage of time in a short time and strengthen the visual attraction. However, since it takes a long time and requires the stable shooting, it is a great challenge for the photographer. In this article, we propose a time-lapse photography system with virtual and real robots. To help users shoot time-lapse videos efficiently, we first parameterize the time-lapse photography and propose a parameter optimization method. For different parameters, different aesthetic models, including image and video aesthetic quality assessment networks, are used to generate optimal parameters. Then we propose a time-lapse photography interface to facilitate users to view and adjust parameters and use virtual robots to conduct virtual photography in a three-dimensional scene. The system can also export the parameters and provide them to real robots so that the time-lapse videos can be filmed in the real world. In addition, we propose a time-lapse photography aesthetic assessment method that can automatically evaluate the aesthetic quality of time-lapse video. The experimental results show that our method can efficiently obtain the time-lapse videos. We also conduct a user study. The results show that our system has the similar effect as professional photographers and is more efficient.

cs.CV↗

arXiv2022

Design of Switchable Frequency-Selective Rasorber with A-R-A-T or A-T-A-R Operating Modes

This work presents a switchable frequency-selective rasorber (SFSR) with two operating modes. Switching from transmission to reflection can be achieved by appropriately adding feeders and PIN diodes based on cascaded two-dimensional lossy and lossless frequency-selective surface (FSS) screens. The proposed SFSR can realize out-of-band absorption. Analysis of the equivalent circuit model (ECM) can be useful for achieving a switchable rasorber. As the state of the PIN diodes changes, the working state of the SFSR can be switched from low-frequency reflection and high-frequency transmission to low-frequency transmission and high-frequency reflection, respectively. In the final-revision simulation of the working band of the SFSR, in the ON state, reflection and transmission peaks of -0.55 dB at 4.06 GHz and -0.52 dB at 5.97 GHz, respectively, are achieved; in the OFF state, the transmission and reflection peaks of -0.31 dB at 4.04GHz and -0.26 dB at 6.01 GHz, respectively, are obtained. A prototype sample is developed and validated. The results are in good agreement with those of the full-wave simulation. The proposed design can be used in intelligent anti-jamming communication.

physics.app-ph↗

arXiv2022

Aesthetic Visual Question Answering of Photographs

Aesthetic assessment of images can be categorized into two main forms: numerical assessment and language assessment. Aesthetics caption of photographs is the only task of aesthetic language assessment that has been addressed. In this paper, we propose a new task of aesthetic language assessment: aesthetic visual question and answering (AVQA) of images. If we give a question of images aesthetics, model can predict the answer. We use images from \textit{www.flickr.com}. The objective QA pairs are generated by the proposed aesthetic attributes analysis algorithms. Moreover, we introduce subjective QA pairs that are converted from aesthetic numerical labels and sentiment analysis from large-scale pre-train models. We build the first aesthetic visual question answering dataset, AesVQA, that contains 72,168 high-quality images and 324,756 pairs of aesthetic questions. Two methods for adjusting the data distribution have been proposed and proved to improve the accuracy of existing models. This is the first work that both addresses the task of aesthetic VQA and introduces subjectiveness into VQA tasks. The experimental results reveal that our methods outperform other VQA models on this new task.

cs.CV↗

arXiv2022

Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning

In recent years, image generation has made great strides in improving the quality of images, producing high-fidelity ones. Also, quite recently, there are architecture designs, which enable GAN to unsupervisedly learn the semantic attributes represented in different layers. However, there is still a lack of research on generating face images more consistent with human aesthetics. Based on EigenGAN [He et al., ICCV 2021], we build the techniques of reinforcement learning into the generator of EigenGAN. The agent tries to figure out how to alter the semantic attributes of the generated human faces towards more preferable ones. To accomplish this, we trained an aesthetics scoring model that can conduct facial beauty prediction. We also can utilize this scoring model to analyze the correlation between face attributes and aesthetics scores. Empirically, using off-the-shelf techniques from reinforcement learning would not work well. So instead, we present a new variant incorporating the ingredients emerging in the reinforcement learning communities in recent years. Compared to the original generated images, the adjusted ones show clear distinctions concerning various attributes. Experimental results using the MindSpore, show the effectiveness of the proposed method. Altered facial images are commonly more attractive, with significantly improved aesthetic levels.

cs.CV↗

arXiv2022

Aesthetic Attributes Assessment of Images with AMANv2 and DPC-CaptionsV2

Image aesthetic quality assessment is popular during the last decade. Besides numerical assessment, nature language assessment (aesthetic captioning) has been proposed to describe the generally aesthetic impression of an image. In this paper, we propose aesthetic attribute assessment, which is the aesthetic attributes captioning, i.e., to assess the aesthetic attributes such as composition, lighting usage and color arrangement. It is a non-trivial task to label the comments of aesthetic attributes, which limit the scale of the corresponding datasets. We construct a novel dataset, named DPC-CaptionsV2, by a semi-automatic way. The knowledge is transferred from a small-scale dataset with full annotations to large-scale professional comments from a photography website. Images of DPC-CaptionsV2 contain comments up to 4 aesthetic attributes: composition, lighting, color, and subject. Then, we propose a new version of Aesthetic Multi-Attributes Networks (AMANv2) based on the BUTD model and the VLPSA model. AMANv2 fuses features of a mixture of small-scale PCCD dataset with full annotations and large-scale DPCCaptionsV2 dataset with full annotations. The experimental results of DPCCaptionsV2 show that our method can predict the comments on 4 aesthetic attributes, which are closer to aesthetic topics than those produced by the previous AMAN model. Through the evaluation criteria of image captioning, the specially designed AMANv2 model is better to the CNN-LSTM model and the AMAN model.

cs.CV↗

arXiv2022

Aesthetic Language Guidance Generation of Images Using Attribute Comparison

With the vigorous development of mobile photography technology, major mobile phone manufacturers are scrambling to improve the shooting ability of equipments and the photo beautification algorithm of software. However, the improvement of intelligent equipments and algorithms cannot replace human subjective photography technology. In this paper, we propose the aesthetic language guidance of image (ALG). We divide ALG into ALG-T and ALG-I according to whether the guiding rules are based on photography templates or guidance images. Whether it is ALG-T or ALG-I, we guide photography from three attributes of color, lighting and composition of the images. The differences of the three attributes between the input images and the photography templates or the guidance images are described in natural language, which is aesthetic natural language guidance (ALG). Also, because of the differences in lighting and composition between landscape images and portrait images, we divide the input images into landscape images and portrait images. Both ALG-T and ALG-I conduct aesthetic language guidance respectively for the two types of input images (landscape images and portrait images).

cs.CV↗

arXiv2022

Meta Clustering Learning for Large-scale Unsupervised Person Re-identification

Unsupervised Person Re-identification (U-ReID) with pseudo labeling recently reaches a competitive performance compared to fully-supervised ReID methods based on modern clustering algorithms. However, such clustering-based scheme becomes computationally prohibitive for large-scale datasets. How to efficiently leverage endless unlabeled data with limited computing resources for better U-ReID is under-explored. In this paper, we make the first attempt to the large-scale U-ReID and propose a "small data for big task" paradigm dubbed Meta Clustering Learning (MCL). MCL only pseudo-labels a subset of the entire unlabeled data via clustering to save computing for the first-phase training. After that, the learned cluster centroids, termed as meta-prototypes in our MCL, are regarded as a proxy annotator to softly annotate the rest unlabeled data for further polishing the model. To alleviate the potential noisy labeling issue in the polishment phase, we enforce two well-designed loss constraints to promise intra-identity consistency and inter-identity strong correlation. For multiple widely-used U-ReID benchmarks, our method significantly saves computational cost while achieving a comparable or even better performance compared to prior works.

cs.CV↗

arXiv2022

Learning with Recoverable Forgetting

Life-long learning aims at learning a sequence of tasks without forgetting the previously acquired knowledge. However, the involved training data may not be life-long legitimate due to privacy or copyright reasons. In practical scenarios, for instance, the model owner may wish to enable or disable the knowledge of specific tasks or specific samples from time to time. Such flexible control over knowledge transfer, unfortunately, has been largely overlooked in previous incremental or decremental learning methods, even at a problem-setup level. In this paper, we explore a novel learning scheme, termed as Learning wIth Recoverable Forgetting (LIRF), that explicitly handles the task- or sample-specific knowledge removal and recovery. Specifically, LIRF brings in two innovative schemes, namely knowledge deposit and withdrawal, which allow for isolating user-designated knowledge from a pre-trained network and injecting it back when necessary. During the knowledge deposit process, the specified knowledge is extracted from the target network and stored in a deposit module, while the insensitive or general knowledge of the target network is preserved and further augmented. During knowledge withdrawal, the taken-off knowledge is added back to the target network. The deposit and withdraw processes only demand for a few epochs of finetuning on the removal data, ensuring both data and time efficiency. We conduct experiments on several datasets, and demonstrate that the proposed LIRF strategy yields encouraging results with gratifying generalization capability.

cs.CV↗

arXiv2022

Image Coding for Machines with Omnipotent Feature Learning

Image Coding for Machines (ICM) aims to compress images for AI tasks analysis rather than meeting human perception. Learning a kind of feature that is both general (for AI tasks) and compact (for compression) is pivotal for its success. In this paper, we attempt to develop an ICM framework by learning universal features while also considering compression. We name such features as omnipotent features and the corresponding framework as Omni-ICM. Considering self-supervised learning (SSL) improves feature generalization, we integrate it with the compression task into the Omni-ICM framework to learn omnipotent features. However, it is non-trivial to coordinate semantics modeling in SSL and redundancy removing in compression, so we design a novel information filtering (IF) module between them by co-optimization of instance distinguishment and entropy minimization to adaptively drop information that is weakly related to AI tasks (e.g., some texture redundancy). Different from previous task-specific solutions, Omni-ICM could directly support AI tasks analysis based on the learned omnipotent features without joint training or extra transformation. Albeit simple and intuitive, Omni-ICM significantly outperforms existing traditional and learning-based codecs on multiple fundamental vision tasks.

cs.CV↗

arXiv2022

Mandheling: Mixed-Precision On-Device DNN Training with DSP Offloading

This paper proposes Mandheling, the first system that enables highly resource-efficient on-device training by orchestrating the mixed-precision training with on-chip Digital Signal Processing (DSP) offloading. Mandheling fully explores the advantages of DSP in integer-based numerical calculation by four novel techniques: (1) a CPU-DSP co-scheduling scheme to mitigate the overhead from DSP-unfriendly operators; (2) a self-adaptive rescaling algorithm to reduce the overhead of dynamic rescaling in backward propagation; (3) a batch-splitting algorithm to improve the DSP cache efficiency; (4) a DSP-compute subgraph reusing mechanism to eliminate the preparation overhead on DSP. We have fully implemented Mandheling and demonstrate its effectiveness through extensive experiments. The results show that, compared to the state-of-the-art DNN engines from TFLite and MNN, Mandheling reduces the per-batch training time by 5.5$\times$ and the energy consumption by 8.9$\times$ on average. In end-to-end training tasks, Mandheling reduces up to 10.7$\times$ convergence time and 13.1$\times$ energy consumption, with only 1.9%-2.7% accuracy loss compared to the FP32 precision setting.

cs.NI↗

arXiv2022

Aesthetic Attribute Assessment of Images Numerically on Mixed Multi-attribute Datasets

With the continuous development of social software and multimedia technology, images have become a kind of important carrier for spreading information and socializing. How to evaluate an image comprehensively has become the focus of recent researches. The traditional image aesthetic assessment methods often adopt single numerical overall assessment scores, which has certain subjectivity and can no longer meet the higher aesthetic requirements. In this paper, we construct an new image attribute dataset called aesthetic mixed dataset with attributes(AMD-A) and design external attribute features for fusion. Besides, we propose a efficient method for image aesthetic attribute assessment on mixed multi-attribute dataset and construct a multitasking network architecture by using the EfficientNet-B0 as the backbone network. Our model can achieve aesthetic classification, overall scoring and attribute scoring. In each sub-network, we improve the feature extraction through ECA channel attention module. As for the final overall scoring, we adopt the idea of the teacher-student network and use the classification sub-network to guide the aesthetic overall fine-grain regression. Experimental results, using the MindSpore, show that our proposed method can effectively improve the performance of the aesthetic overall and attribute assessment.

cs.CV↗

arXiv2022

Short Video Uprising: How #BlackLivesMatter Content on TikTok Challenges the Protest Paradigm

This study uses TikTok (N = 8,173) to examine how short-form video platforms challenge the protest paradigm in the recent Black Lives Matter movement. A computer-mediated visual analysis, computer vision, is employed to identify the presence of four visual frames of protest (riot, confrontation, spectacle, and debate) in multimedia content. Results of descriptive statistics and the t-test indicate that the three delegitimizing frames - riot, confrontation, and spectacle - are rarely found on TikTok, whereas the debate frame, that empowers marginalized communities, dominates the public sphere. However, although the three delegitimizing frames receive lower social media visibility, as measured by views, likes, shares, followers, and durations, legitimizing elements, such as the debate frame, minority identities, and unofficial sources, are not generally favored by TikTok audiences. This study concludes that while short-form video platforms could potentially challenge the protest paradigm on the content creators' side, the audiences' preference as measured by social media visibility might still be moderately associated with the protest paradigm.

cs.CY↗

arXiv2022

Edge Security: Challenges and Issues

Edge computing is a paradigm that shifts data processing services to the network edge, where data are generated. While such an architecture provides faster processing and response, among other benefits, it also raises critical security issues and challenges that must be addressed. This paper discusses the security threats and vulnerabilities emerging from the edge network architecture spanning from the hardware layer to the system layer. We further discuss privacy and regulatory compliance challenges in such networks. Finally, we argue the need for a holistic approach to analyze edge network security posture, which must consider knowledge from each layer.

cs.CR↗